rename .qmd files into groups

inst/assets/_book.yml

@@ -5,32 +5,32 @@ book:
     - pages/preamble.qmd
     - part: "Introduction"
       chapters:
-        - pages/introduction.qmd
-        - pages/spatial-transcriptomics.qmd
-        - pages/bioconductor-data-classes.qmd
+        - pages/bkg-introduction.qmd
+        - pages/bkg-spatial-transcriptomics.qmd
+        - pages/bkg-bioconductor-data-classes.qmd
     - part: "Sequencing-based platforms"
       chapters:
-        - pages/analysis-steps.qmd
-        - pages/load-sequencing-based-data.qmd
-        - pages/quality-control.qmd
-        - pages/normalization.qmd
-        - pages/feature-selection.qmd
-        - pages/dimensionality-reduction.qmd
-        - pages/clustering.qmd
-        - pages/spot-deconvolution.qmd
-        - pages/spatial-co-localization.qmd
-        - pages/differential-expression.qmd
-        - pages/workflows-sequencing-based.qmd
-        - pages/workflow-human-dlpfc.qmd
-        - pages/workflow-mouse-coronal.qmd
-        - pages/workflow-spatialibd.qmd
+        - pages/seq-analysis-steps.qmd
+        - pages/seq-load-data.qmd
+        - pages/seq-quality-control.qmd
+        - pages/seq-normalization.qmd
+        - pages/seq-feature-selection.qmd
+        - pages/seq-dimensionality-reduction.qmd
+        - pages/seq-clustering.qmd
+        - pages/seq-spot-deconvolution.qmd
+        - pages/seq-spatial-co-localization.qmd
+        - pages/seq-differential-expression.qmd
+        - pages/seq-workflows.qmd
+        - pages/seq-workflow-human-dlpfc.qmd
+        - pages/seq-workflow-mouse-coronal.qmd
+        - pages/seq-workflow-spatialibd.qmd
     - part: "Imaging-based platforms"
       chapters:
-        - pages/load-imaging-based-data.qmd
-        - pages/workflows-imaging-based.qmd
+        - pages/img-load-data.qmd
+        - pages/img-workflows.qmd
   appendices:
-    - pages/related-resources.qmd
-    - pages/acknowledgments.qmd
+    - pages/apx-related-resources.qmd
+    - pages/apx-acknowledgments.qmd
   cover-image: assets/cover.png
   favicon: assets/favicon.png
   sidebar: 

inst/pages/acknowledgments.qmd → inst/pages/apx-acknowledgments.qmd

@@ -1,4 +1,4 @@
-# Acknowledgments {#sec-acknowledgments}
+# Acknowledgments {#sec-apx-acknowledgments}
 
 ## Contributors
 

inst/pages/related-resources.qmd → inst/pages/apx-related-resources.qmd

@@ -1,4 +1,4 @@
-# Related resources {#sec-related-resources}
+# Related resources {#sec-apx-related-resources}
 
 
 ## Overview
@@ -27,7 +27,7 @@ Workflows and other resources for other spatial omics platforms:
 
 ## Data structures
 
-Data structures for storing spatial transcriptomics and other spatial omics data that have not already been discussed in @sec-bioconductor-data-classes:
+Data structures for storing spatial transcriptomics and other spatial omics data that have not already been discussed in @sec-bkg-bioconductor-data-classes:
 
 - [**AnnData**](https://anndata.readthedocs.io/en/latest/): Python class for storing single-cell and spatial data within the [scverse](https://scverse.org/) framework.
 

inst/pages/bioconductor-data-classes.qmd → inst/pages/bkg-bioconductor-data-classes.qmd

@@ -1,4 +1,4 @@
-# Bioconductor data classes {#sec-bioconductor-data-classes}
+# Bioconductor data classes {#sec-bkg-bioconductor-data-classes}
 
 ## Overview
 

inst/pages/introduction.qmd → inst/pages/bkg-introduction.qmd

@@ -1,4 +1,4 @@
-# Introduction {#sec-introduction}
+# Introduction {#sec-bkg-introduction}
 
 ## Overview
 

inst/pages/spatial-transcriptomics.qmd → inst/pages/bkg-spatial-transcriptomics.qmd

@@ -1,4 +1,4 @@
-# Spatial transcriptomics {#sec-spatial-transcriptomics}
+# Spatial transcriptomics {#sec-bkg-spatial-transcriptomics}
 
 ## Overview
 

inst/pages/load-imaging-based-data.qmd → inst/pages/img-load-data.qmd

@@ -1,10 +1,10 @@
-# Load imaging-based data {#sec-load-imaging-based-data}
+# Load imaging-based data {#sec-img-load-data}
 
 ## Overview
 
 In this chapter, we load spatial transcriptomics datasets from imaging-based platforms (10x Genomics Xenium, Vizgen MERSCOPE, and/or NanoString CosMx), which will be used in the examples in the following chapters.
 
-For details on the technological platforms, see @sec-bioconductor-data-classes.
+For details on the technological platforms, see @sec-bkg-bioconductor-data-classes.
 
 
 ## References {.unnumbered}

inst/pages/workflows-imaging-based.qmd → inst/pages/img-workflows.qmd

@@ -1,3 +1,3 @@
-# Workflows for imaging-based data {#sec-workflows-imaging-based}
+# Workflows for imaging-based data {#sec-img-workflows}
 
 The following chapters contain examples of extended analysis workflows for data from imaging-based platforms.

inst/pages/analysis-steps.qmd → inst/pages/seq-analysis-steps.qmd

@@ -1,10 +1,10 @@
-# Analysis steps {#sec-analysis-steps}
+# Analysis steps {#sec-seq-analysis-steps}
 
 This part contains chapters describing individual analysis steps within computational analysis workflows for spatial transcriptomics data from sequencing-based platforms.
 
 Each chapter describes the analysis type, including discussion on statistical issues and available methods, and provides an interactive example with R code and an example dataset.
 
-In the next chapter, we load a dataset in `SpatialExperiment` format (see @sec-bioconductor-data-classes), which will be used in several of the subsequent chapters.
+In the next chapter, we load a dataset in `SpatialExperiment` format (see @sec-bkg-bioconductor-data-classes), which will be used in several of the subsequent chapters.
 
 The last few chapters in this part also contain examples of complete analysis workflows for selected datasets and technological platforms.
 

inst/pages/clustering.qmd → inst/pages/seq-clustering.qmd

@@ -1,4 +1,4 @@
-# Clustering {#sec-clustering}
+# Clustering {#sec-seq-clustering}
 
 
 ## Overview
@@ -15,7 +15,7 @@ Once we have identified spatial domains, these can then be further investigated
 
 ## Load data from previous steps
 
-We start by loading the data object(s) saved after running the analysis steps from the previous chapters. Code to re-run the previous steps is shown in condensed form in @sec-analysis-steps.
+We start by loading the data object(s) saved after running the analysis steps from the previous chapters. Code to re-run the previous steps is shown in condensed form in @sec-seq-analysis-steps.
 
 ```{r, message=FALSE, results='hide'}
 library(SpatialExperiment)
@@ -92,11 +92,11 @@ In many ST datasets, we can uncover further structure by performing analyses tha
 
 ### Clustering using SVGs
 
-One way to perform spatially-aware clustering is to first perform spatially-aware feature selection to identify a set of top spatially variable genes (SVGs) (see @sec-feature-selection) and then use the set of top SVGs as the input for clustering.
+One way to perform spatially-aware clustering is to first perform spatially-aware feature selection to identify a set of top spatially variable genes (SVGs) (see @sec-seq-feature-selection) and then use the set of top SVGs as the input for clustering.
 
 In this case, the spatial information is taken into account during the feature selection stage, where we select a set of top SVGs instead of top HVGs. For the clustering stage, we can use the same algorithms as for non-spatial clustering.
 
-Here, we demonstrate an example using [nnSVG](https://bioconductor.org/packages/nnSVG) [@Weber2023] to select the set of top SVGs. Note that in this example, we run nnSVG using a small subset of the dataset for faster runtime. For a full analysis, the full dataset should be used (see @sec-feature-selection for more details).
+Here, we demonstrate an example using [nnSVG](https://bioconductor.org/packages/nnSVG) [@Weber2023] to select the set of top SVGs. Note that in this example, we run nnSVG using a small subset of the dataset for faster runtime. For a full analysis, the full dataset should be used (see @sec-seq-feature-selection for more details).
 
 ```{r, message=FALSE}
 library(nnSVG)

inst/pages/differential-expression.qmd → inst/pages/seq-differential-expression.qmd

@@ -1,4 +1,4 @@
-# Differential expression {#sec-differential-expression}
+# Differential expression {#sec-seq-differential-expression}
 
 ## Overview
 
@@ -7,7 +7,7 @@ In this chapter, we perform differential expression testing between clusters or
 
 ## Load data from previous steps
 
-We start by loading the data object(s) saved after running the analysis steps from the previous chapters. Code to re-run the previous steps is shown in condensed form in @sec-analysis-steps.
+We start by loading the data object(s) saved after running the analysis steps from the previous chapters. Code to re-run the previous steps is shown in condensed form in @sec-seq-analysis-steps.
 
 ```{r, message=FALSE, results='hide'}
 library(SpatialExperiment)

inst/pages/dimensionality-reduction.qmd → inst/pages/seq-dimensionality-reduction.qmd

@@ -1,4 +1,4 @@
-# Dimensionality reduction {#sec-dimensionality-reduction}
+# Dimensionality reduction {#sec-seq-dimensionality-reduction}
 
 
 ## Overview
@@ -9,7 +9,7 @@ In this chapter, we apply dimensionality reduction methods to visualize the data
 
 ## Load data from previous steps
 
-We start by loading the data object(s) saved after running the analysis steps from the previous chapters. Code to re-run the previous steps is shown in condensed form in @sec-analysis-steps.
+We start by loading the data object(s) saved after running the analysis steps from the previous chapters. Code to re-run the previous steps is shown in condensed form in @sec-seq-analysis-steps.
 
 ```{r, message=FALSE, results='hide'}
 library(SpatialExperiment)

inst/pages/feature-selection.qmd → inst/pages/seq-feature-selection.qmd

@@ -1,4 +1,4 @@
-# Feature selection {#sec-feature-selection}
+# Feature selection {#sec-seq-feature-selection}
 
 
 ## Overview
@@ -9,7 +9,7 @@ Here we apply feature selection methods to identify highly variable genes (HVGs)
 
 ## Load data from previous steps
 
-We start by loading the data object(s) saved after running the analysis steps from the previous chapters. Code to re-run the previous steps is shown in condensed form in @sec-analysis-steps.
+We start by loading the data object(s) saved after running the analysis steps from the previous chapters. Code to re-run the previous steps is shown in condensed form in @sec-seq-analysis-steps.
 
 ```{r, message=FALSE, results='hide'}
 library(SpatialExperiment)
@@ -129,7 +129,7 @@ rowData(spe_nnSVG)$gene_name[which(rowData(spe_nnSVG)$rank == 1)]
 
 ### Downstream analyses
 
-The set of top SVGs from nnSVG may then be investigated further, e.g. by plotting the spatial expression of several top genes and by comparing the list of top genes with known gene sets associated with biological processes of interest in the dataset. The set of top SVGs may also be used as the input for further downstream analyses such as spatially-aware clustering to define spatial domains (see @sec-clustering).
+The set of top SVGs from nnSVG may then be investigated further, e.g. by plotting the spatial expression of several top genes and by comparing the list of top genes with known gene sets associated with biological processes of interest in the dataset. The set of top SVGs may also be used as the input for further downstream analyses such as spatially-aware clustering to define spatial domains (see @sec-seq-clustering).
 
 
 ## References {.unnumbered}

inst/pages/load-sequencing-based-data.qmd → inst/pages/seq-load-data.qmd

@@ -1,8 +1,8 @@
-# Load sequencing-based data {#sec-load-sequencing-based-data}
+# Load sequencing-based data {#sec-seq-load-data}
 
 ## Overview
 
-In the following chapters, we apply analysis methods to spatial transcriptomics datasets from sequencing-based platforms, which are formatted as `SpatialExperiment` objects or objects from other Bioconductor data classes (see @sec-bioconductor-data-classes).
+In the following chapters, we apply analysis methods to spatial transcriptomics datasets from sequencing-based platforms, which are formatted as `SpatialExperiment` objects or objects from other Bioconductor data classes (see @sec-bkg-bioconductor-data-classes).
 
 Here, we load a 10x Genomics Visium dataset that will be used in several of the following chapters.
 
@@ -15,7 +15,7 @@ This dataset is available for download in `SpatialExperiment` format from the [S
 
 This dataset consists of one sample (Visium capture area) from one donor, consisting of postmortem human brain tissue from the dorsolateral prefrontal cortex (DLPFC) brain region, measured with the 10x Genomics Visium platform. The dataset is described in the original publication by @Maynard2021.
 
-More details on the dataset are also included in @sec-workflow-human-dlpfc.
+More details on the dataset are also included in @sec-seq-workflow-human-dlpfc.
 
 <!-- To do: move additional details on dataset here (from DLPFC workflow chapter) -->
 
@@ -35,7 +35,7 @@ spe <- Visium_humanDLPFC()
 
 ## SpatialExperiment object
 
-Check the structure of the `SpatialExperiment` object. For more details on the `SpatialExperiment` structure, see @sec-bioconductor-data-classes.
+Check the structure of the `SpatialExperiment` object. For more details on the `SpatialExperiment` structure, see @sec-bkg-bioconductor-data-classes.
 
 ```{r, message=FALSE}
 # check object

inst/pages/normalization.qmd → inst/pages/seq-normalization.qmd

@@ -1,4 +1,4 @@
-# Normalization {#sec-normalization}
+# Normalization {#sec-seq-normalization}
 
 
 ## Overview
@@ -8,7 +8,7 @@ Here we apply normalization methods developed for scRNA-seq data, treating each
 
 ## Load data from previous steps
 
-We start by loading the data object(s) saved after running the analysis steps from the previous chapters. Code to re-run the previous steps is shown in condensed form in @sec-analysis-steps.
+We start by loading the data object(s) saved after running the analysis steps from the previous chapters. Code to re-run the previous steps is shown in condensed form in @sec-seq-analysis-steps.
 
 ```{r, message=FALSE, results='hide'}
 library(SpatialExperiment)

inst/pages/quality-control.qmd → inst/pages/seq-quality-control.qmd

@@ -1,4 +1,4 @@
-# Quality control {#sec-quality-control}
+# Quality control {#sec-seq-quality-control}
 
 
 ## Overview
@@ -25,7 +25,7 @@ The first three characteristics listed above are also used for QC in scRNA-seq d
 
 ## Load data from previous steps
 
-We start by loading the data object(s) saved after running the analysis steps from the previous chapters. Code to re-run the previous steps is shown in condensed form in @sec-analysis-steps.
+We start by loading the data object(s) saved after running the analysis steps from the previous chapters. Code to re-run the previous steps is shown in condensed form in @sec-seq-analysis-steps.
 
 ```{r, message=FALSE, results='hide'}
 library(SpatialExperiment)
@@ -343,7 +343,7 @@ Removing the spots containing zero cells (2% of spots) would also be problematic
 
 The sections above consider quality control at the spot level. In some datasets, it may also be appropriate to apply quality control procedures or filtering at the gene level. For example, certain genes may be biologically irrelevant for downstream analyses.
 
-However, here we make a distinction between quality control and feature selection. Removing biologically uninteresting genes (such as mitochondrial genes) may also be considered as part of feature selection, since there is no underlying experimental procedure that has failed. Therefore, we will discuss gene-level filtering in the @sec-feature-selection chapter.
+However, here we make a distinction between quality control and feature selection. Removing biologically uninteresting genes (such as mitochondrial genes) may also be considered as part of feature selection, since there is no underlying experimental procedure that has failed. Therefore, we will discuss gene-level filtering in the @sec-seq-feature-selection chapter.
 
 
 ## References {.unnumbered}

inst/pages/spatial-co-localization.qmd → inst/pages/seq-spatial-co-localization.qmd

@@ -1,4 +1,4 @@
-# Spatial co-localization {#sec-spatial-co-localization}
+# Spatial co-localization {#sec-seq-spatial-co-localization}
 
 
 ## Overview
@@ -10,7 +10,7 @@ For these analyses, we will use a different dataset that provides single-cell sp
 
 ## Load data from previous steps
 
-We start by loading the data object(s) saved after running the analysis steps from the previous chapters. Code to re-run the previous steps is shown in condensed form in @sec-analysis-steps.
+We start by loading the data object(s) saved after running the analysis steps from the previous chapters. Code to re-run the previous steps is shown in condensed form in @sec-seq-analysis-steps.
 <!--
 ```{r, message=FALSE, results='hide'}
 

inst/pages/spot-deconvolution.qmd → inst/pages/seq-spot-deconvolution.qmd

@@ -1,8 +1,8 @@
-# Spot deconvolution {#sec-spot-deconvolution}
+# Spot deconvolution {#sec-seq-spot-deconvolution}
 
 ## Overview
 
-Spot-level ST data (e.g. from the 10x Genomics Visium platform) can contain zero, one, or multiple cells per spot, depending on the spatial resolution of the platform and the tissue cell density. This characteristic of the data affects several steps in analysis workflows, including quality control (@sec-quality-control) and clustering (@sec-clustering).
+Spot-level ST data (e.g. from the 10x Genomics Visium platform) can contain zero, one, or multiple cells per spot, depending on the spatial resolution of the platform and the tissue cell density. This characteristic of the data affects several steps in analysis workflows, including quality control (@sec-seq-quality-control) and clustering (@sec-seq-clustering).
 
 Note that this is also a characteristic of ST data that is distinct from single-cell RNA sequencing data, so here we cannot easily apply existing methods from single-cell workflows.
 
@@ -13,7 +13,7 @@ In this section, we will demonstrate methods to deconvolve cell types per spot.
 
 ## Load data from previous steps
 
-We start by loading the data object(s) saved after running the analysis steps from the previous chapters. Code to re-run the previous steps is shown in condensed form in @sec-analysis-steps.
+We start by loading the data object(s) saved after running the analysis steps from the previous chapters. Code to re-run the previous steps is shown in condensed form in @sec-seq-analysis-steps.
 
 ```{r, message=FALSE, results='hide'}
 library(SpatialExperiment)